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Hedwig : A named entity linker

Klang, Marcus LU orcid and Nugues, Pierre LU orcid (2020) 12th International Conference on Language Resources and Evaluation, LREC 2020 In LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings p.4501-4508
Abstract

Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.

Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Named entity annotation, Named entity linking, Named entity recognition
host publication
LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
series title
LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
editor
Calzolari, Nicoletta ; Bechet, Frederic ; Blache, Philippe ; Choukri, Khalid ; Cieri, Christopher ; Declerck, Thierry ; Goggi, Sara ; Isahara, Hitoshi ; Maegaard, Bente ; Mariani, Joseph ; Mazo, Helene ; Moreno, Asuncion ; Odijk, Jan and Piperidis, Stelios
pages
8 pages
publisher
European Language Resources Association
conference name
12th International Conference on Language Resources and Evaluation, LREC 2020
conference location
Marseille, France
conference dates
2020-05-11 - 2020-05-16
external identifiers
  • scopus:85096580434
ISBN
9791095546344
language
English
LU publication?
yes
id
1bd6c347-8ed2-4c32-923f-a352af1bff6a
alternative location
https://www.aclweb.org/anthology/2020.lrec-1.554.pdf
date added to LUP
2020-12-08 11:29:34
date last changed
2022-04-19 02:35:13
@inproceedings{1bd6c347-8ed2-4c32-923f-a352af1bff6a,
  abstract     = {{<p>Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.</p>}},
  author       = {{Klang, Marcus and Nugues, Pierre}},
  booktitle    = {{LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings}},
  editor       = {{Calzolari, Nicoletta and Bechet, Frederic and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, Helene and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios}},
  isbn         = {{9791095546344}},
  keywords     = {{Named entity annotation; Named entity linking; Named entity recognition}},
  language     = {{eng}},
  pages        = {{4501--4508}},
  publisher    = {{European Language Resources Association}},
  series       = {{LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings}},
  title        = {{Hedwig : A named entity linker}},
  url          = {{https://www.aclweb.org/anthology/2020.lrec-1.554.pdf}},
  year         = {{2020}},
}